# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
"""FasterRcnn-DCN positive and negative sample screening for RPN."""

import numpy as np
from mindspore import nn
from mindspore.common import dtype as mstype
from mindspore.common.tensor import Tensor
from mindspore.ops import operations as P


class BboxAssignSample(nn.Cell):
    """
    Bbox assigner and sampler definition.

    Args:
        config (dict): Config.
        batch_size (int): Batchsize.
        num_bboxes (int): The anchor nums.
        add_gt_as_proposals (bool): add gt bboxes as proposals flag.

    Returns:
        Tensor, output tensor.
        bbox_targets: bbox location, (batch_size, num_bboxes, 4)
        bbox_weights: bbox weights, (batch_size, num_bboxes, 1)
        labels: label for every bboxes, (batch_size, num_bboxes, 1)
        label_weights: label weight for every bboxes, (batch_size, num_bboxes, 1)

    Examples:
        BboxAssignSample(config, 2, 1024, True)
    """

    def __init__(self, config, batch_size, num_bboxes, add_gt_as_proposals):
        super(BboxAssignSample, self).__init__()
        cfg = config
        self.dtype = np.float32
        self.ms_type = mstype.float32
        self.batch_size = batch_size

        self.neg_iou_thr = Tensor(cfg.neg_iou_thr, self.ms_type)
        self.pos_iou_thr = Tensor(cfg.pos_iou_thr, self.ms_type)
        self.min_pos_iou = Tensor(cfg.min_pos_iou, self.ms_type)
        self.zero_thr = Tensor(0.0, self.ms_type)

        self.num_bboxes = num_bboxes
        self.num_gts = cfg.num_gts
        self.num_expected_pos = cfg.num_expected_pos
        self.num_expected_neg = cfg.num_expected_neg
        self.add_gt_as_proposals = add_gt_as_proposals

        if self.add_gt_as_proposals:
            self.label_inds = Tensor(np.arange(1, self.num_gts + 1))

        self.concat = P.Concat(axis=0)
        self.max_gt = P.ArgMaxWithValue(axis=0)
        self.max_anchor = P.ArgMaxWithValue(axis=1)
        self.sum_inds = P.ReduceSum()
        self.iou = P.IOU()
        self.greaterequal = P.GreaterEqual()
        self.greater = P.Greater()
        self.select = P.Select()
        self.gatherND = P.GatherNd()
        self.squeeze = P.Squeeze()
        self.cast = P.Cast()
        self.logicaland = P.LogicalAnd()
        self.less = P.Less()
        self.random_choice_with_mask_pos = P.RandomChoiceWithMask(self.num_expected_pos)
        self.random_choice_with_mask_neg = P.RandomChoiceWithMask(self.num_expected_neg)
        self.reshape = P.Reshape()
        self.equal = P.Equal()
        self.bounding_box_encode = P.BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0))
        self.scatterNdUpdate = P.ScatterNdUpdate()
        self.scatterNd = P.ScatterNd()
        self.logicalnot = P.LogicalNot()
        self.tile = P.Tile()
        self.zeros_like = P.ZerosLike()

        self.assigned_gt_inds = Tensor(np.full(num_bboxes, -1, dtype=np.int32))
        self.assigned_gt_zeros = Tensor(np.array(np.zeros(num_bboxes), dtype=np.int32))
        self.assigned_gt_ones = Tensor(np.array(np.ones(num_bboxes), dtype=np.int32))
        self.assigned_gt_ignores = Tensor(np.full(num_bboxes, -1, dtype=np.int32))
        self.assigned_pos_ones = Tensor(np.array(np.ones(self.num_expected_pos), dtype=np.int32))

        self.check_neg_mask = Tensor(np.array(np.ones(self.num_expected_neg - self.num_expected_pos), dtype=np.bool))
        self.range_pos_size = Tensor(np.arange(self.num_expected_pos).astype(self.dtype))
        self.check_gt_one = Tensor(np.full((self.num_gts, 4), -1, dtype=self.dtype))
        self.check_anchor_two = Tensor(np.full((self.num_bboxes, 4), -2, dtype=self.dtype))

    def construct(self, gt_bboxes_i, gt_labels_i, valid_mask, bboxes, gt_valids):
        """construct"""
        gt_bboxes_i = self.select(self.cast(self.tile(self.reshape(self.cast(gt_valids, mstype.int32), \
                                  (self.num_gts, 1)), (1, 4)), mstype.bool_), gt_bboxes_i, self.check_gt_one)
        bboxes = self.select(self.cast(self.tile(self.reshape(self.cast(valid_mask, mstype.int32), \
                             (self.num_bboxes, 1)), (1, 4)), mstype.bool_), bboxes, self.check_anchor_two)

        overlaps = self.iou(bboxes, gt_bboxes_i)

        max_overlaps_w_gt_index, max_overlaps_w_gt = self.max_gt(overlaps)
        _, max_overlaps_w_ac = self.max_anchor(overlaps)

        neg_sample_iou_mask = self.logicaland(self.greaterequal(max_overlaps_w_gt, self.zero_thr), \
                                              self.less(max_overlaps_w_gt, self.neg_iou_thr))
        assigned_gt_inds2 = self.select(neg_sample_iou_mask, self.assigned_gt_zeros, self.assigned_gt_inds)

        pos_sample_iou_mask = self.greaterequal(max_overlaps_w_gt, self.pos_iou_thr)
        assigned_gt_inds3 = self.select(pos_sample_iou_mask, \
                                        max_overlaps_w_gt_index + self.assigned_gt_ones, assigned_gt_inds2)
        assigned_gt_inds4 = assigned_gt_inds3
        for j in range(self.num_gts):
            max_overlaps_w_ac_j = max_overlaps_w_ac[j:j+1:1]
            overlaps_w_gt_j = self.squeeze(overlaps[j:j+1:1, ::])

            pos_mask_j = self.logicaland(self.greaterequal(max_overlaps_w_ac_j, self.min_pos_iou), \
                                         self.equal(overlaps_w_gt_j, max_overlaps_w_ac_j))

            assigned_gt_inds4 = self.select(pos_mask_j, self.assigned_gt_ones + j, assigned_gt_inds4)

        assigned_gt_inds5 = self.select(valid_mask, assigned_gt_inds4, self.assigned_gt_ignores)

        pos_index, valid_pos_index = self.random_choice_with_mask_pos(self.greater(assigned_gt_inds5, 0))

        pos_check_valid = self.cast(self.greater(assigned_gt_inds5, 0), self.ms_type)
        pos_check_valid = self.sum_inds(pos_check_valid, -1)
        valid_pos_index = self.less(self.range_pos_size, pos_check_valid)
        pos_index = pos_index * self.reshape(self.cast(valid_pos_index, mstype.int32), (self.num_expected_pos, 1))

        pos_assigned_gt_index = self.gatherND(assigned_gt_inds5, pos_index) - self.assigned_pos_ones
        pos_assigned_gt_index = pos_assigned_gt_index * self.cast(valid_pos_index, mstype.int32)
        pos_assigned_gt_index = self.reshape(pos_assigned_gt_index, (self.num_expected_pos, 1))

        neg_index, valid_neg_index = self.random_choice_with_mask_neg(self.equal(assigned_gt_inds5, 0))

        num_pos = self.cast(self.logicalnot(valid_pos_index), self.ms_type)
        num_pos = self.sum_inds(num_pos, -1)
        unvalid_pos_index = self.less(self.range_pos_size, num_pos)
        valid_neg_index = self.logicaland(self.concat((self.check_neg_mask, unvalid_pos_index)), valid_neg_index)

        pos_bboxes_ = self.gatherND(bboxes, pos_index)
        pos_gt_bboxes_ = self.gatherND(gt_bboxes_i, pos_assigned_gt_index)
        pos_gt_labels = self.gatherND(gt_labels_i, pos_assigned_gt_index)

        pos_bbox_targets_ = self.bounding_box_encode(pos_bboxes_, pos_gt_bboxes_)

        valid_pos_index = self.cast(valid_pos_index, mstype.int32)
        valid_neg_index = self.cast(valid_neg_index, mstype.int32)
        bbox_targets_total = self.scatterNd(pos_index, pos_bbox_targets_, (self.num_bboxes, 4))
        bbox_weights_total = self.scatterNd(pos_index, valid_pos_index, (self.num_bboxes,))
        labels_total = self.scatterNd(pos_index, pos_gt_labels, (self.num_bboxes,))
        total_index = self.concat((pos_index, neg_index))
        total_valid_index = self.concat((valid_pos_index, valid_neg_index))
        label_weights_total = self.scatterNd(total_index, total_valid_index, (self.num_bboxes,))

        return bbox_targets_total, self.cast(bbox_weights_total, mstype.bool_), \
               labels_total, self.cast(label_weights_total, mstype.bool_)
